Introduction to geospatial data

• One estimate says that 100 TB of only weather data are generated every single day
  • This means there is a lot of data to work with!
  • Note that this is also problematic, since it can be difficult to work with such large datasets
• Geospatial data is used in a variety of fields
  • Agriculture
  • Urban planning
  • Environmental science
  • Public health
  • Transportation
  • And many more!

The amount of geospatial data is useful for SAE

• Geospatial data can be highly predictive of e.g. poverty
  • Urbanity
  • Land class/cover
  • Vegetation indices
  • Population counts
  • etc. etc.
• More importantly: it’s available everywhere!

Think of what you need for SAE

• You need a sample, e.g. a household survey
  • This will only cover some of the country
    
    
• You need auxiliary data that is:
  • Predictive of the outcome you care about
  • Available throughout the entire country
    
    
• Some countries, use administrative data
  • But, importantly, it’s often not available or is of low quality!

A quick example

• Let’s take a look at Malawi

• Why Malawi?

  • I have survey data you can use 😃
  • Only going to use part of Malawi for this example (size of data)

• Consider the 2019/2020 Integrated Household Survey (IHS5)

  • Was used for the Malawi Poverty Report 2020
  • Can say things about poverty at the district level
  • If you want to split by urban/rural, only at the region level

A quick example

Malawi admin areas - Northern region only

Observations at the district and TA level

Sub-area model with sectors

• One option: estimate a sub-area model at the EA level!

• Steps:

  • Collapse survey data to the EA level
  • Extract geospatial data at the EA level
  • Estimate the model